Tagged: scala

In January of last year we decided as a company to move towards containerization and began a migration to move onto AWS ECS. We pushed to move to containers, and off of AMI based VM deployments, in order to speed up our deployments, simplify our build tooling (since it only has to work on containers), get the benefits of being able to run our production code in a sandbox even locally on our dev machines (something you can’t really do easily with AMI’s), and lower our costs by getting more out of the resources we’re already paying for.

However, making ECS production ready was actually quite the challenge. In this post I’ll discuss:

Today I ran into a fascinating bug. We use ficus as a HOCON auto parser for scala. It works great, because parsing configurations into strongly typed case classes is annoying. Ficus works by using a macro to invoke implicitly in scope Reader[T] classes for data types and recursively builds the nested parser.

Our config verifier just invokes the hocon parser and makes sure it doesn’t throw an error. ProductsConfig has a lot of fields to it, and I recently added a new one. Suddenly the test broke with the following error:

For the past several years I’ve been thinking about the idea of an open source workflow execution engine. Something like AWS workflow but simpler. No need to upload python, or javascript, or whatever. Just call an API with a callback url, and when the API completes its step, callback to the coordinator with a payload. Have the coordinator then send that payload to the next step in the workflow, etc.

This kind of simplified workflow process is really common and I keep running into it at different places that I work at. For example, my company ingests client catalogs to augment imagery with their SKU numbers and other metadata. However that ingestion process is really fragmented and asynchronous. There’s an ingestion step, following that there is a normalization step, then a processing step, then an indexing step, etc. In the happy case everyone is hooked together with a queue pipeline … Read more

A coworker of mine and I frequently talk about higher kinded types, category theory, and lament about the lack of unified types in scala: namely functors. A functor is a fancy name for a thing that can be mapped on. Wanting to abstract over something that is mappable comes up more often than you think. I don’t necessarily care that its an Option, or a List, or a whatever. I just care that it has a map.

We’re not the only ones who want this. Cats, Shapeless, Scalaz, all have implementations of functor. The downside there is that usually these definitions tend to leak throughout your ecosystem. I’ve written before about ecosystem and library management, and it’s an important thing to think about when working at a company of 50+ people. You need to think long and hard about putting dependencies on things. Sometimes you can, if those libraries have … Read more

We like to think that building a service ecosystem is like stacking building blocks. You start with a function in your code. That function is hosted in a class. That class in a service. That service is hosted in a cluster. That cluster in a region. That region in a data center, etc. At each level there’s a myriad of challenges.

From the start, developers tend to use things like logging and metrics to debug their systems, but a certain class of problems crops up when you need to debug across services. From a debugging perspective, you’d like to have a higher projection of the view of the system: a linearized view of what requests are doing. I.e. You want to be able to see that service A called service B and service C called service D at the granularity of single requests.… Read more

There are a million and one ways to do (micro-)services, each with a million and one pitfalls. At Curalate, we’ve been on a long journey of splitting out our monolith into composable and simple services. It’s never easy, as there are a lot of advantages to having a monolith. Things like refactoring, code-reuse, deployment, versioning, rollbacks, are all atomic in a monolith. But there are a lot of disadvantages as well. Monoliths encourage poor factoring, bugs in one part of the codebase force rollbacks/changes of the entire application, reasoning about the application in general becomes difficult, build times are slow, transient build errors increase, etc.

To that end our first foray into services was built on top of Twitter Finagle stack. If you go to the page and can’t figure out what exactly finagle does, I don’t blame you. The documentation is … Read more

Yet another SOA blog post, this time about calling services. I’ve seen a lot of posts, articles, even books, on how to write services but not a good way about calling services. It may seem trivial, isn’t calling a service a matter of making a web request to one? Yes, it is, but in a larger organization it’s not always so trivial.

Distributing fat clients

The problem I ran into was the service stack in use at my organization provided a feature rich client as an artifact of a services build. It had retries, metrics, tracing with zipkin, etc. But, it also pulled in things like finagle, netty, jackson, and each service may be distributing slightly different versions of all of these dependencies. When you start to consume 3, 4, 5 or more clients in your own service, suddenly you’ve gotten into an intractable mess of dependencies. Sometimes there’s no … Read more

Haven’t posted in a while, since I’ve been heads down in building a lot of cool tooling at work (blog posts coming), but had a chance to mess around a bit with something that came up in an interview question this week.

I frequently ask candidates a high level design question to build PacMan. Games like pacman are fun because on the surface they are very simple, but if you don’t structure your entities and their interactions correctly the design falls apart.

At some point during the interview we had scaled the question up such that there was now a problem of knowing at a particular point in the game what was nearby it. For example, if the board is 100000 x 100000 (10 billion elements) how efficiently can we determine if there is a nugget/wall next to us? One option is to store all of these entities in a … Read more

When building service architectures one thing you need to solve is how to pass context between services. This is usually stuff like request id’s and other tracing information (maybe you use zipkin) between service calls. This means that if you set request id FooBar123 on an entrypoint to service A, if service A calls service B it should know that the request id is still FooBar123. The bigger challenge is usually making sure that all thread locals keep this around (and across futures/execution contexts), but before you attempt that you need to get it into the system in the first place.

I’m working in finatra these days, and I love this framework. It’s got all the things I loved from dropwizard but in a scala first way. Todays challenge was that I wanted to be able to pass request http headers around between services in a typesafe way that … Read more

I was recently exploring shapeless and a coworker turned me onto the interesting features of coproducts and how they can be used with polymorphic functions.

Frequently when using pattern matching you want to make sure that all cases are exhaustively checked. A non exhaustive pattern match is a runtime exception waiting to happen. As a scala user, I’m all about compile time checking. For classes that I own I can enforce exhaustiveness by creating a sealed trait heirarchy:

And if I ever try and match on an Base type I’ll get a compiler warning (that I can fail on) if all the types aren’t matched. This is nice because if I ever add another type, I’ll get a (hopefully) failed build.